Hydrologic Model Evaluation and Assessment of Projected Climate Change Impacts Using Bias-Corrected Stream Flows
Hydrologic models driven by downscaled meteorologic data from general circulation models (GCM) should be evaluated using long-term simulations over a historical period. However, simulations driven by GCM data cannot be directly evaluated using observed flows, and the confidence in the results can be...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/12/8/2312 |
| _version_ | 1797557397002125312 |
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| author | Joseph A. Daraio |
| author_facet | Joseph A. Daraio |
| author_sort | Joseph A. Daraio |
| collection | DOAJ |
| description | Hydrologic models driven by downscaled meteorologic data from general circulation models (GCM) should be evaluated using long-term simulations over a historical period. However, simulations driven by GCM data cannot be directly evaluated using observed flows, and the confidence in the results can be relatively low. The objectives of this paper were to bias correct simulated stream flows from calibrated hydrologic models for two basins in New Jersey, USA, and evaluate model performance in comparison to uncorrected simulations. Then, we used stream flow bias correction and flow duration curves (FDCs) to evaluate and assess simulations driven by statistically downscaled GCMs for the historical period and the future time slices 2041–2070 and 2071–2099. Bias correction of stream flow from simulations increased confidence in the performance of two previously calibrated hydrologic models. Results indicated there was no difference in projected FDCs for uncorrected and bias-corrected flows in one basin, while this was not the case in the second basin. This result provided greater confidence in projected stream flow changes in the former basin and implied more uncertainty in projected stream flows in the latter. Applications in water resources can use the methods described to evaluate the performance of GCM-driven simulations and assess the potential impacts of climate change with an appropriate level of confidence in the model results. |
| first_indexed | 2024-03-10T17:16:29Z |
| format | Article |
| id | doaj.art-a3467dab9c50457aa461b41b0bdffe45 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T17:16:29Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-a3467dab9c50457aa461b41b0bdffe452023-11-20T10:28:45ZengMDPI AGWater2073-44412020-08-01128231210.3390/w12082312Hydrologic Model Evaluation and Assessment of Projected Climate Change Impacts Using Bias-Corrected Stream FlowsJoseph A. Daraio0Department of Civil Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, CanadaHydrologic models driven by downscaled meteorologic data from general circulation models (GCM) should be evaluated using long-term simulations over a historical period. However, simulations driven by GCM data cannot be directly evaluated using observed flows, and the confidence in the results can be relatively low. The objectives of this paper were to bias correct simulated stream flows from calibrated hydrologic models for two basins in New Jersey, USA, and evaluate model performance in comparison to uncorrected simulations. Then, we used stream flow bias correction and flow duration curves (FDCs) to evaluate and assess simulations driven by statistically downscaled GCMs for the historical period and the future time slices 2041–2070 and 2071–2099. Bias correction of stream flow from simulations increased confidence in the performance of two previously calibrated hydrologic models. Results indicated there was no difference in projected FDCs for uncorrected and bias-corrected flows in one basin, while this was not the case in the second basin. This result provided greater confidence in projected stream flow changes in the former basin and implied more uncertainty in projected stream flows in the latter. Applications in water resources can use the methods described to evaluate the performance of GCM-driven simulations and assess the potential impacts of climate change with an appropriate level of confidence in the model results.https://www.mdpi.com/2073-4441/12/8/2312flow duration curvesbias correction of stream flowsflow regimeprecipitation-runoff modeling system (PRMS) |
| spellingShingle | Joseph A. Daraio Hydrologic Model Evaluation and Assessment of Projected Climate Change Impacts Using Bias-Corrected Stream Flows Water flow duration curves bias correction of stream flows flow regime precipitation-runoff modeling system (PRMS) |
| title | Hydrologic Model Evaluation and Assessment of Projected Climate Change Impacts Using Bias-Corrected Stream Flows |
| title_full | Hydrologic Model Evaluation and Assessment of Projected Climate Change Impacts Using Bias-Corrected Stream Flows |
| title_fullStr | Hydrologic Model Evaluation and Assessment of Projected Climate Change Impacts Using Bias-Corrected Stream Flows |
| title_full_unstemmed | Hydrologic Model Evaluation and Assessment of Projected Climate Change Impacts Using Bias-Corrected Stream Flows |
| title_short | Hydrologic Model Evaluation and Assessment of Projected Climate Change Impacts Using Bias-Corrected Stream Flows |
| title_sort | hydrologic model evaluation and assessment of projected climate change impacts using bias corrected stream flows |
| topic | flow duration curves bias correction of stream flows flow regime precipitation-runoff modeling system (PRMS) |
| url | https://www.mdpi.com/2073-4441/12/8/2312 |
| work_keys_str_mv | AT josephadaraio hydrologicmodelevaluationandassessmentofprojectedclimatechangeimpactsusingbiascorrectedstreamflows |